Discovering Multi-type Correlated Events with Time Series for Exception Detection of Complex Systems
| Autor: | Peng Xun, Cun-Lu Li, Peidong Zhu, Haoyang Zhu |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Series (mathematics)
business.industry Computer science Complex system Cloud computing 02 engineering and technology Type (model theory) computer.software_genre Data set 020204 information systems Correlation analysis 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Time series business computer |
| Zdroj: | ICDM Workshops |
| DOI: | 10.1109/icdmw.2016.0012 |
| Popis: | With the increase of systems' complexity, exception detection becomes more important and difficult. For most complex systems, like cloud platform, exception detection is mainly conducted by analyzing a large amount of telemetry data collected from systems at runtime. Time series data and events data are two major types of telemetry data. Techniques of correlation analysis are important tools that are widely used by engineers for data-driven exception detection. Despite their importance, there has been little previous work addressing the correlations between two types of heterogeneous data for exception detection: continuous time series data and temporal events data. In this paper, we propose an approach to discovery the correlation between multi-type time series data and multi-type events data. Correlations between multi-type events data and multi-type time series data are used to detect systems' exceptions. Our experimental results on real data sets demonstrate the effectiveness of our method for exception detection. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|